Abstract
In this paper, we discuss attribute-value reduction for raising up the understandability of data and rules. In the traditional “reduction” sense, the goal is to find the smallest number of attributes such that they enable us to discern each tuple or each decision class. However, once we pay attention also to the number of attribute values, that is, the size/resolution of each attribute domain, another goal appears.
An interesting question is like, which one is better in the following two situations 1) we can discern individual tuples with a single attribute described in fine granularity, and 2) we can do this with a few attributes described in rough granularity. Such a question is related to understandability and Kansei expression of data as well as rules. We propose a criterion and an algorithm to find near-optimal solutions for the criterion. In addition, we show some illustrative results for some databases in UCI repository of machine learning databases.
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References
Nagamachi, M.: Kansei Engineering: A new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics 15, 3–11 (1995)
Tsutsumi, K., Ito, N., Hashimoto, H.: A Development of the Building Kansei Information Retrieval System. In: Xth International Conference on Computing in Civil and Building Engineering, June 2-4, 2004, pp. 174–175 (2004)
Yanagisawa, H., Fukuda, S.: Interactive Reduct Evolutional Computation for Aesthetic Design. Journal of Computing and Information Science in Engneering 5(1), 1–7 (2005)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Ziarko, W.: Variable Precision Rough Set Model. Journal of Computer and System Sciences 46, 39–59 (1993)
Muto, Y., Kudo, M.: Discernibility-Based Variable Granularity and Kansei Representations. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 692–700. Springer, Heidelberg (2005)
Muto, Y., Kudo, M., Murai, T.: Reduction of Attribute Values for Kansei Representation. Journal of Advanced Computational Intelligence and Intelligent Informatics 10(5), 666–672 (2006)
Zadeh, L.A.: Fuzzy Sets and Information Granularity. In: Advances in Fuzzy Set Theory and Applications, pp. 3–18 (1979)
Lin, T.Y.: Granular Computing on Binary Relation I: Data Mining and Neighborhood Systems. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications, pp. 107–121, pp. 122–140. Physica-Verlag, Heidelberg (1998)
Murai, T., et al.: Granular Reasoning Using Zooming In & Out: Part 1. Propositional Reasoning. In: Wang, G., et al. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 421–424. Springer, Heidelberg (2003)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Newman, D.J., et al.: UCI Repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, CA (1998), http://www.ics.uci.edu/mlearn/MLRepository.html
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Muto, Y., Kudo, M., Shidara, Y. (2007). Reduction of Categorical and Numerical Attribute Values for Understandability of Data and Rules. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_26
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DOI: https://doi.org/10.1007/978-3-540-72458-2_26
Publisher Name: Springer, Berlin, Heidelberg
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